{"id":"W2800417082","doi":"10.17269/s41997-018-0068-z","title":"The “Lac-Mégantic tragedy” seen through the lens of the EnRiCH Community Resilience Framework for High-Risk Populations","year":2018,"lang":"en","type":"article","venue":"Canadian Journal of Public Health","topic":"Disaster Response and Management","field":"Health Professions","cited_by":17,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Ottawa; Université de Sherbrooke; Université du Québec à Chicoutimi; Health and Social Services Centre University Institute of Geriatrics of Sherbrooke","funders":"","keywords":"Community resilience; Public relations; Upstream (networking); Community engagement; Local government; Population; Business; Psychological resilience; Government (linguistics); Environmental planning; Community organization; Political science; Environmental resource management; Public administration; Geography; Psychology; Engineering; Environmental health; Medicine; Economics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007158752,0.0008334927,0.0006365111,0.003247652,0.02415542,0.01389178,0.002861137,0.004691291,0.01064143],"category_scores_gemma":[0.009196565,0.000340818,0.0006333722,0.002106205,0.06572586,0.01140177,0.02095083,0.01046707,0.0004831207],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01749529,"about_ca_system_score_gemma":0.04037107,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1664872,"about_ca_topic_score_gemma":0.3290919,"domain_scores_codex":[0.9954901,0.00229172,0.00006913309,0.0002979214,0.0005176218,0.001333372],"domain_scores_gemma":[0.9955447,0.001176808,0.0002263338,0.0003642585,0.0005184104,0.0021695],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"qualitative","study_design_scores_codex":[0.00001653333,0.0000250178,0.001358407,0.00006606429,0.00001310134,0.0004092665,0.09373526,0.0004040328,0.0001013993,0.8729309,0.0204444,0.01049572],"study_design_scores_gemma":[0.00001701379,0.00003378615,0.002673176,0.0004476828,0.0000206995,0.0004079733,0.2472777,0.0005576967,0.0000987788,0.5563584,0.1920534,0.00005381857],"study_design_candidate":"qualitative","study_design_consensus":null,"genre_codex":"commentary","genre_gemma":"empirical","genre_scores_codex":[0.08035694,0.006057529,0.02260252,0.5923582,0.003516348,0.0001604246,0.0003082356,0.0002308576,0.294409],"genre_scores_gemma":[0.9642875,0.002154889,0.004361931,0.01557731,0.0006093109,0.0001555853,0.00006804943,0.00009164478,0.01269371],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8335128,"threshold_uncertainty_score":0.3310363,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2090753334800935,"score_gpt":0.4382671731748953,"score_spread":0.2291918396948018,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}